Abstract
Naïve Bayesian classifiers are well-known for their simplicity and efficiency. They rely on independence hypotheses, together with a normality assumption, which may be too demanding, when dealing with numerical data. Possibility distributions are more compatible with the representation of poor data. This paper investigates two kinds of possibilistic elicitation methods that will be embedded into possibilistic naïve classifiers. The first one is derived from a probability-possibility transformation of Gaussian distributions (or mixtures of them), which introduces some further tolerance. The second kind is based on a direct interpretation of data in fuzzy histogram or possibilistic formats that exploit an idea of proximity between attribute values in different ways. Besides, possibilistic classifiers may be allowed to leave the classification open between several classes in case of insufficient information for choosing one (which may be of interest when the number of classes is large). The experiments reported show the interest of possibilistic classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Denton, A., Perrizo, W.: A kernel-based semi-naive bayesian classifier using p-trees. In: Proc. of the 4th SIAM Inter. Conf. on Data Mining (2004)
Pérez, A., Larraoaga, P., Inza, I.: Bayesian classifiers based on kernel density estimation:flexible classifiers. Inter. J. of Approximate Reasoning 50, 341–362 (2009)
Haouari, B., Ben Amor, N., Elouadi, Z., Mellouli, K.: Naïve possibilistic network classifiers. Fuzzy Set and Systems 160(22), 3224–3238 (2009)
Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. Informatica 31, 249–268 (2007)
Qin, B., Xia, Y.: and Prabhakar S., and Tu Y. A rule-based classification algorithm for uncertain data. In: IEEE International Conference on Data Engineering (2009)
Borgelt, C., Gebhardt, J.: A naïve bayes style possibilistic classifier. In: Proc. 7th European Congress on Intelligent Techniques and Soft Computing, pp. 556–565 (1999)
Borgelt, C., Kruse, R.: Efficient maximum projection of database-induced multivariate possibility distributions. In: Proc. 7th IEEE Int. Conf. on Fuzzy Systems, pp. 663–668 (1998)
Dubois, D., Laurent, F., Gilles, M., Prade, H.: Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing 10, 273–297 (2004)
Dubois, D., Prade, H.: When upper probabilities are possibility measures. Fuzzy sets and Systems 49, 65–74 (1992)
Dubois, D., Prade, H.: On data summarization with fuzzy sets. In: Proc. of the 5th Inter. Fuzzy Systems Assoc. World Congress, IFSA 1993 (1993)
Dubois D. and Prade H. Possibility theory: Qualitative and quantitative aspects. D. Gabbay and P. Smets. editors. Handbook on Defeasible Reasoning and Uncertainty Management Systems, 1:169–226, 1998.
Dubois, D., Prade, H., Sandri, S.: On possibility/probability transformations. In: Fuzzy Logic, pp. 103–112 (1993)
Grossman, D., Dominigos, P.: Learning bayesian maximizing conditional likelihood. In: Proc. on Machine Learning, pp. 46–57 (2004)
John, G.H., Langley, P.: Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (1995)
Jenhani, I., Ben Amor, N., Elouedi, Z.: Decision trees as possibilistic classifiers. Inter. J. of Approximate Reasoning 48(3), 784–807 (2008)
Mertz, J., Murphy, P.M.: Uci repository of machine learning databases, ftp://ftp.ics.uci.edu/pub/machine-learning-databases
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmman, San Francisco (1988)
Yamada, K.: Probability-possibility transformation based on evidence theory. In: IFSA World Congress, vol. 10, pp. 70–75 (2001)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1996)
Cover, T.M., Hart, P.E.: Nearest neighbour pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)
Zaffalon, M.: The naive credal classifier. Journal of statistical planning and inference 105, 5–21 (2002)
Ben Amor, N., Mellouli, K., Benferhat, S., Dubois, D., Prade, H.: A theoretical framework for possibilistic independence in a weakly ordered setting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 117–155 (2002)
Ben Amor, N., Benferhat, S., Elouedi, Z.: Qualitative classification and evaluation in possibilistic decision trees. In: FUZZ-IEEE 2004, vol. 1, pp. 653–657 (2004)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29, 131–161 (1997)
Strauss, O., Comby, F., Aldon, M.J.: Rough histograms for robust statistics. In: Proc. Inter. Conf. on Pattern Recognition (ICPR 2000), Barcelona, pp. II:2684–2687. IEEE Computer Society, Los Alamitos (2000)
Langley, P., Sage, S.: Induction of selective bayesian classifiers. In: Proceedings of 10th Conference on Uncertainty in Artificial Intelligence UAI 1994, pp. 399–406 (1994)
Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: Proceedings of AAAI 1992, vol. 7, pp. 223–228 (1992)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Solomonoff, R.: A formal theory of inductive inference. Information and Control 7, 224–254 (1964)
Benferhat, S., Tabia, K.: An efficient algorithm for naive possibilistic classifiers with uncertain inputs. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 63–77. Springer, Heidelberg (2008)
Sudkamp, T.: Similarity as a foundation for possibility. In: Proc. 9th IEEE Inter. Conf. on Fuzzy Systems, San Antonio, pp. 735–740 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bounhas, M., Mellouli, K., Prade, H., Serrurier, M. (2010). From Bayesian Classifiers to Possibilistic Classifiers for Numerical Data. In: Deshpande, A., Hunter, A. (eds) Scalable Uncertainty Management. SUM 2010. Lecture Notes in Computer Science(), vol 6379. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15951-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-15951-0_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15950-3
Online ISBN: 978-3-642-15951-0
eBook Packages: Computer ScienceComputer Science (R0)